Welcome!
You are invited to take part in a research study being conducted by John Helveston, Ph.D., Assistant Professor in Engineering Management and Systems Engineering at the George Washington University.
Please read this form and ask us any questions that will help you decide if you want to be in the study. Taking part is completely voluntary and even if you decide you want to, you can quit at any time.
You must be at least 18 years old to take part in this study. You are 1 of up to 5,000 people taking part in this study by GWU.
The total expected amount of time you will spend on this study is 10 minutes.
The study has no expected risks.
You are free to skip any questions or stop taking the survey at any point. We are not collecting your name or other identifiable information about you, and all data will be encrypted. The records of this study will be kept private. In any published articles or presentations, we will not include any information that will make it possible to identify you as a subject. Your records for the study may be reviewed by departments of the University responsible for overseeing research safety and compliance. Once all data are collected, anonymized versions of the data will be made publicly available on a repository on GitHub.
You need not respond to the demographic questions at the end, including questions about age, gender identity, ethnicity, and formal education. We collect this information strictly for the purposes of performing demographic comparisons of the data with that of the general public. Survey responses without answers to demographic question may exclude your response from being included in some of these comparisons. Data without demographics may still be presented in some summary totals.
Taking part in this research will not help you directly, however the benefit to society will be a better understanding of people’s preferences for automated transportation modes in the United States.
This survey is made available to respondents via Dynata, which offers great diversity in incentives as some people are motivated by cash, points, or by being able to donate to charity. A cash reward is simply a monetary value in $USD. Points are values that can be traded in for different, non-monetary rewards on the research platform. Finally, donations to a charity of your choice can also be selected as a reward. Dynata aims to respond to all of these individual motivations in order to provide a sample which is diverse and as representative as possible of the target population. Dynata uses a reasonable level of reward based on the amount of effort required, the population, and appropriate regional customs. Regardless of the type of incentive, the value is the same for every respondent in a given study.
Talk to the research team if you have questions, concerns, complaints, or think you have been harmed. You can contact the Principal Investigator listed above at 202-994-7173. For questions regarding your rights as a participant in human research, call the GWU Office of Human Research at 202-994-2715 or contact them via email at ohrirb@gwu.edu.
To ensure anonymity, your signature is not required. Your willingness to participate in this research study is implied if you proceed.
To start, please enter your zip code:
This survey will ask about your preferences for different automated and non-automated transportation modes. To start, let’s learn a little bit more about what automated vehicles are.
Automated vehicles or “driverless cars” are vehicles that are operated by computer systems instead of human drivers. Many cars today include automated features like automatic braking and lane-keeping assistance. In a fully automated vehicle, a computer system would perform all driving tasks with no assistance from a human driver.
The following short video will explain a little more about different levels of vehicle automation and how automated vehicles work.
This survey will ask you to compare nonautomated and automated transportation modes. When the survey describes a mode as automated, it is referring to a level 5 fully-automated vehicle, meaning that the vehicle would drive itself at all times and in all situations without any human assistance.
To start, we’d like to learn a little bit about your current transportation routine and your general thoughts about automated vehicles.
This section includes questions related to your transportation routines and attitudes towards automated vehicles. Given that your transportation routines may have changed due to the COVID-19 pandemic, please consider your transportation routines prior to the pandemic.
| Mode | Use more than before | Use the same as before | Use less than before |
|---|---|---|---|
| Bus (e.g., Metrobus, ART, Ride On) | |||
| Rail (Metrorail or DC streetcar) | |||
| Ride-hailing service (e.g., Lyft, Uber, Via) | |||
| Shared ride-hailing service (e.g., Lyft Shared or UberPool) |
Now that you’ve shared about your current transportation routine, we’d like you to consider a future in which you can choose from various automated and non-automated transportation options.
Let’s learn about these potential transportation options.
Please imagine that in the future, buses could be automated or non-automated.
Automated buses would follow the same pre-determined routes as non-automated buses but would not have a bus driver.
Please imagine that in the future, rail systems (Metrorail, DC Streetcar) would remain non-automated.
They would function the same as they do now and would follow the same routes.
Please imagine that in the future, ride-hailing services could be automated or non-automated
The ride-hailing service would be similar to Uber, Lyft or Via.
You would order a vehicle using a smartphone and could select both your pickup and dropoff locations.
Some of the transportation modes (bus, ride-hailing, shared ride-hailing) could have special features. These features include being automated or having an attendant on board.
Vehicles that are automated would be operated by computer systems with no assistance from a human driver. No option to take control of the vehicle would be available.
Vehicles with an attendant would have a company official on board to help passengers. This attendant would not be responsible for operating the vehicle.
Which of the following options does the image above describe?
Now that you’ve learned about the different potential modes, we would like to learn about your preferences for those modes.
For this next section, imagine you are going out for an evening leisure trip and are deciding how to get there. You will be presented with four different options for transportation modes that you could take. Consider the four options and click on the box to select the transportation option that you would choose.
Let’s start with a practice question.
Imagine you are going out for an evening leisure activity. Which transportation option would you choose?
(Please click on the box for your desired option to select it.)
Now let’s begin the choice tasks. You will be asked 8 questions in total. For each scenario, please imagine that you are going out for an evening leisure activity.
Subjects answered 8 choice questions posed in the manner below. The modes remained fixed for the four options but the attributes (price, total trip time, automation, attendant) were randomized for each question and each participant based on the individual’s respondent ID.
Imagine you are going out on an evening leisure activity. Which transportation option would you choose?
For the previous 8 choice questions, which best describes the type of evening leisure activity you were imagining?
If you selected “Other”, please describe what type of evening leisure activity you were imagining:
We’d like to ask you just a few final demographic questions. We collect the following information to contribute to further data analysis.
In what year were you born? (select from drop-down)
What is your gender?
Please let us know if you have any other thoughts or feedback on this survey. Your feedback will help us make future improvements :)
coefficient | MXL | MXL_weighted |
Lambda | 0.132 (0.008) *** | 0.132 (0.008) *** |
Travel time | -0.438 (0.031) *** | -0.438 (0.031) *** |
Bus | -3.587 (0.445) *** | -3.587 (0.445) *** |
Ride-hailing (RH) | 1.034 (0.546) . | 1.034 (0.546) . |
Shared RH | -3.861 (0.601) *** | -3.861 (0.601) *** |
Bus - Automated | -0.385 (0.609) | -0.385 (0.609) |
Bus - Attendant present | 6.350 (0.888) *** | 6.350 (0.888) *** |
RH - Automated | -2.365 (0.791) ** | -2.365 (0.791) ** |
RH - Attendant present | 9.442 (1.033) *** | 9.442 (1.033) *** |
Shared RH - Automated | -2.755 (0.795) *** | -2.755 (0.795) *** |
Shared RH - Attendant present | 5.432 (0.918) *** | 5.432 (0.918) *** |
travelTime_sigma | 0.542 (0.040) *** | 0.542 (0.040) *** |
mode_bus_sigma | 8.120 (0.687) *** | 8.120 (0.687) *** |
bus_automated_yes_sigma | -7.426 (0.919) *** | -7.426 (0.919) *** |
bus_attendant_yes_sigma | -12.608 (1.315) *** | -12.608 (1.315) *** |
mode_RH_sigma | 13.441 (0.847) *** | 13.441 (0.847) *** |
RH_automated_yes_sigma | -13.871 (1.118) *** | -13.871 (1.118) *** |
RH_attendant_yes_sigma | -19.070 (1.650) *** | -19.070 (1.650) *** |
mode_sharedRH_sigma | 11.345 (0.831) *** | 11.345 (0.831) *** |
sharedRH_automated_yes_sigma | -11.329 (0.950) *** | -11.329 (0.950) *** |
sharedRH_attendant_yes_sigma | -14.539 (1.411) *** | -14.539 (1.411) *** |
#> a flextable object.
#> col_keys: `coefficient`, `MXL`, `MXL_weighted`
#> header has 1 row(s)
#> body has 21 row(s)
#> original dataset sample:
#> coefficient MXL MXL_weighted
#> 1 Lambda 0.132 (0.008) *** 0.132 (0.008) ***
#> 2 Travel time -0.438 (0.031) *** -0.438 (0.031) ***
#> 3 Bus -3.587 (0.445) *** -3.587 (0.445) ***
#> 4 Ride-hailing (RH) 1.034 (0.546) . 1.034 (0.546) .
#> 5 Shared RH -3.861 (0.601) *** -3.861 (0.601) ***
modelResults_mxl <- round(summary(mxl_wtp)$statTable, 1)
modelResults_mxl_weighted <- round(summary(mxl_wtp_weighted)$statTable, 1)
modelResults_mxl$par <- row.names(modelResults_mxl)
modelResults_mxl_weighted$par <- row.names(modelResults_mxl_weighted)
modelResults_full <- modelResults_mxl %>%
left_join(modelResults_mxl_weighted, by = "par") %>%
select("par", ".x", ".y")
names(modelResults_full) <- c("Attribute", "MXL", "MXL weighted")
summary2 <- flextable(as.data.frame(modelResults_full))
theme_vanilla(summary2)
Attribute | MXL | MXL weighted |
Log-Likelihood: | -16,484.5 | -15,087.7 |
Null Log-Likelihood: | -19,252.9 | -17,647.0 |
AIC: | 33,011.0 | 30,217.3 |
BIC: | 33,169.3 | 30,375.6 |
McFadden R2: | 0.1 | 0.1 |
Adj McFadden R2: | 0.1 | 0.1 |
Number of Observations: | 13,888.0 | 13,888.0 |
Number of Clusters | 1,736.0 | 1,736.0 |
summary2 <- align(summary2, align = "right", part = "body")
#print(summary2, preview = "docx")
#> obsID predicted_prob predicted_prob_lower predicted_prob_upper scenario_type
#> 1 1 0.2041418 0.1896820 0.2192739 baseline
#> 2 1 0.4558193 0.4333491 0.4800577 baseline
#> 3 1 0.2167830 0.1987817 0.2334358 baseline
#> 4 1 0.1232558 0.1112397 0.1356208 baseline
#> 5 2 0.2076513 0.1930493 0.2236371 baseline
#> 6 2 0.4498273 0.4271962 0.4740837 baseline
#> scenario_num altID mode automated attendant price travelTime
#> 1 1 1 bus No No 2 27
#> 2 1 2 rail No No 2 15
#> 3 1 3 RH No No 13 15
#> 4 1 4 sharedRH No No 10 20
#> 5 2 1 bus No No 2 40
#> 6 2 2 rail No No 2 28
#> price_reduction mode_bus mode_RH mode_sharedRH automated_No automated_Yes
#> 1 1 1 0 0 1 0
#> 2 1 0 0 0 1 0
#> 3 1 0 1 0 1 0
#> 4 1 0 0 1 1 0
#> 5 1 1 0 0 1 0
#> 6 1 0 0 0 1 0
#> attendant_No attendant_Yes bus_automated_yes bus_automated_no
#> 1 1 0 0 1
#> 2 1 0 0 0
#> 3 1 0 0 0
#> 4 1 0 0 0
#> 5 1 0 0 1
#> 6 1 0 0 0
#> bus_attendant_yes bus_attendant_no RH_automated_yes RH_automated_no
#> 1 0 1 0 0
#> 2 0 0 0 0
#> 3 0 0 0 1
#> 4 0 0 0 0
#> 5 0 1 0 0
#> 6 0 0 0 0
#> RH_attendant_yes RH_attendant_no sharedRH_automated_yes sharedRH_automated_no
#> 1 0 0 0 0
#> 2 0 0 0 0
#> 3 0 1 0 0
#> 4 0 0 0 1
#> 5 0 0 0 0
#> 6 0 0 0 0
#> sharedRH_attendant_yes sharedRH_attendant_no percent_red
#> 1 0 0 0
#> 2 0 0 0
#> 3 0 0 0
#> 4 0 1 0
#> 5 0 0 0
#> 6 0 0 0